crossllingual

Cross-lingual refers to the ability or process of transferring information, knowledge, or models across different languages. It is a key concept in multilingual natural language processing (NLP) and machine learning, aiming to bridge language barriers and facilitate the sharing of resources and understanding among diverse linguistic communities.

In the context of NLP, cross-lingual techniques involve training models on data in one language (or multiple languages) and applying them to tasks in other languages. This approach is particularly useful when labeled data in certain languages is limited or unavailable. Common applications include cross-lingual text classification, machine translation, information retrieval, and question answering.

Methods to achieve cross-lingual transfer often leverage shared representations of words, sentences, or concepts. One prevalent technique involves using multilingual embeddings where words from different languages are mapped into a common vector space, allowing models to understand and process multiple languages simultaneously. Cross-lingual transfer learning can also utilize bilingual or multilingual resources such as parallel corpora, which contain aligned texts in different languages.

The development of multilingual transformer models, such as mBERT and XLM-R, has advanced cross-lingual NLP by enabling models trained on multiple languages to perform well even with limited data in target languages. These models learn language-agnostic features that facilitate knowledge transfer across languages, improving performance on zero-shot or few-shot tasks.

Overall, cross-lingual methods play a vital role in making language technologies more inclusive and accessible, promoting communication and information sharing across linguistic boundaries.